Commercialization of Stem Cell Therapeutic Research: Bridging a Big Gap
Bibliographic record
Abstract
ABSTRACT Stem cell therapeutic research is passing through a transition phase between laboratory research and health industry. According to the US data registry of clinical trials, more than 4776 studies have been registered, 2882 have been completed whereas 1894 studies are in process. Surprisingly, in spite of having huge research, there are two commercialized stem cell therapeutic products in global market and these two products are also not approved by FDA. As it has been discussed in literature, stem cells have been considered as promising candidates to treat non-curable diseases like cancer etc. More than 80% successful clinical trials have been done showing no or little side effects with much better efficiency than pharmacokinetics but still stem cell research is far from being commercialized. The major reason of stem cells non-commercialization is the gap among clinicians, researchers, industry experts and policy makers. A multibillion dollar grants and a very strong communication system between doctors, researchers, industrial experts, policy makers, regulating authorities, are the pre-requisite to commercialize stem cell therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".